code: def keypoints_distance(detected_pose, tracked_pose):
      distances = np.linalg.norm(
          detected_pose.points - tracked_pose.estimate, axis=1
      )
      match_num = np.count_nonzero(
          (distances < keypoint_dist_threshold)
          * (detected_pose.scores > detection_threshold)
          * (tracked_pose.last_detection.scores > detection_threshold)
      )
      return 1 / (1 + match_num)
  
imports: import os
  import time
  from queue import Queue
  from threading import Thread
  import cv2
  import numpy
  import torch
  import torch.multiprocessing
  from alphapose.utils.pPose_nms import pose_nms, write_json
  from alphapose.utils.transforms import get_func_heatmap_to_coord
  import norfair
label_desc: Use image node_id 1 for calling a global function with example usage: keypoints_distance(detected_pose, tracked_pose) and returns: unknown